3 code implementations • 19 Oct 2023 • Xavier Puig, Eric Undersander, Andrew Szot, Mikael Dallaire Cote, Tsung-Yen Yang, Ruslan Partsey, Ruta Desai, Alexander William Clegg, Michal Hlavac, So Yeon Min, Vladimír Vondruš, Theophile Gervet, Vincent-Pierre Berges, John M. Turner, Oleksandr Maksymets, Zsolt Kira, Mrinal Kalakrishnan, Jitendra Malik, Devendra Singh Chaplot, Unnat Jain, Dhruv Batra, Akshara Rai, Roozbeh Mottaghi
We present Habitat 3. 0: a simulation platform for studying collaborative human-robot tasks in home environments.
no code implementations • 20 Jun 2023 • Sriram Yenamandra, Arun Ramachandran, Karmesh Yadav, Austin Wang, Mukul Khanna, Theophile Gervet, Tsung-Yen Yang, Vidhi Jain, Alexander William Clegg, John Turner, Zsolt Kira, Manolis Savva, Angel Chang, Devendra Singh Chaplot, Dhruv Batra, Roozbeh Mottaghi, Yonatan Bisk, Chris Paxton
HomeRobot (noun): An affordable compliant robot that navigates homes and manipulates a wide range of objects in order to complete everyday tasks.
no code implementations • 27 Jun 2022 • Allen Z. Ren, Bharat Govil, Tsung-Yen Yang, Karthik Narasimhan, Anirudha Majumdar
Robust and generalized tool manipulation requires an understanding of the properties and affordances of different tools.
no code implementations • 5 Mar 2022 • Tsung-Yen Yang, Tingnan Zhang, Linda Luu, Sehoon Ha, Jie Tan, Wenhao Yu
In this paper, we propose a safe reinforcement learning framework that switches between a safe recovery policy that prevents the robot from entering unsafe states, and a learner policy that is optimized to complete the task.
no code implementations • NeurIPS 2021 • Tsung-Yen Yang, Michael Hu, Yinlam Chow, Peter J. Ramadge, Karthik Narasimhan
We then develop an agent with a modular architecture that can interpret and adhere to such textual constraints while learning new tasks.
no code implementations • ICLR 2020 • Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, Peter J. Ramadge
We consider the problem of learning control policies that optimize a reward function while satisfying constraints due to considerations of safety, fairness, or other costs.
no code implementations • 20 Jun 2020 • Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, Peter J. Ramadge
We consider the problem of reinforcement learning when provided with (1) a baseline control policy and (2) a set of constraints that the learner must satisfy.
no code implementations • Findings of the Association for Computational Linguistics 2020 • Tsung-Yen Yang, Andrew S. Lan, Karthik Narasimhan
Learning representations of spatial references in natural language is a key challenge in tasks like autonomous navigation and robotic manipulation.